Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data

Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data

Abstract Background There is currently no consensus on the impact of class imbalance methods on the performance of clinical prediction models. We aimed to empirically investigate the impact of random oversampling and random undersampling, two commonly used class imbalance methods, on the internal an...

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Bibliographic Details
Main Authors: Cynthia Yang, Egill A. Fridgeirsson, Jan A. Kors, Jenna M. Reps, Peter R. Rijnbeek
Format: Article
Language:English
Published: SpringerOpen 2024-01-01
Series:Journal of Big Data
Subjects:
Patient-level prediction
Clinical prediction model
Class Imbalance Problem
Machine learning
External validation
Clinical decision support
Online Access:https://doi.org/10.1186/s40537-023-00857-7

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